Engineering a Conformant Probabilistic Planner

We present a partial-order, conformant, probabilistic planner, Probapop which competed in the blind track of the Probabilistic Planning Competition in IPC-4. We explain how we adapt distance based heuristics for use with probabilistic domains. Probapop also incorporates heuristics based on probability of success. We explain the successes and difficulties encountered during the design and implementation of Probapop

Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

Warmstarting of Model-based Algorithm Configuration

The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.Comment: Preprint of AAAI'18 pape

Leveraging probabilistic reasoning in deterministic planning for large-scale autonomous Search-and-Tracking

Search-And-Tracking (SaT) is the problem of searching for a mobile target and tracking it once it is found. Since SaT platforms face many sources of uncertainty and operational constraints, progress in the field has been restricted to simple and unrealistic scenarios. In this paper, we propose a new hybrid approach to SaT that allows us to successfully address large-scale and complex SaT missions. The probabilistic structure of SaT is compiled into a deterministic planning model and Bayesian inference is directly incorporated in the planning mechanism. Thanks to this tight integration between automated planning and probabilistic reasoning, we are able to exploit the power of both approaches. Planning provides the tools to efficiently explore big search spaces, while Bayesian inference, by readily combining prior knowledge with observable data, allows the planner to make more informed and effective decisions. We offer experimental evidence of the potential of our approach

Component-based synthesis of motion planning algorithms

Combinatory Logic Synthesis generates data or runnable programs according to formal type specifications. Synthesis results are composed based on a user-specified repository of components, which brings several advantages for representing spaces of high variability. This work suggests strategies to manage the resulting variations by proposing a domain-specific brute-force search and a machine learning-based optimization procedure. The brute-force search involves the iterative generation and evaluation of machining strategies. In contrast, machine learning optimization uses statistical models to enable the exploration of the design space. The approaches involve synthesizing programs and meta-programs that manipulate, run, and evaluate programs. The methodologies are applied to the domain of motion planning algorithms, and they include the configuration of programs belonging to different algorithmic families. The study of the domain led to the identification of variability points and possible variations. Proof-of-concept repositories represent these variability points and incorporate them into their semantic structure. The selected algorithmic families involve specific computation steps or data structures, and corresponding software components represent possible variations. Experimental results demonstrate that CLS enables synthesis-driven domain-specific optimization procedures to solve complex problems by exploring spaces of high variability.Combinatory Logic Synthesis (CLS) generiert Daten oder lauffähige Programme anhand von formalen Typspezifikationen. Die Ergebnisse der Synthese werden auf Basis eines benutzerdefinierten Repositories von Komponenten zusammengestellt, was diverse Vorteile für die Beschreibung von Räumen mit hoher Variabilität mit sich bringt. Diese Arbeit stellt Strategien für den Umgang mit den resultierenden Variationen vor, indem eine domänen-spezifische Brute-Force Suche und ein maschinelles Lernverfahren für die Untersuchung eines Optimierungsproblems aufgezeigt werden. Die Brute-Force Suche besteht aus der iterativen Generierung und Evaluation von Frässtrategien. Im Gegensatz dazu nutzt der Optimierungsansatz statistische Modelle zur Erkundung des Entwurfsraums. Beide Ansätze synthetisieren Programme und Metaprogramme, welche Programme bearbeiten, ausführen und evaluieren. Diese Methoden werden auf die Domäne der Bewegungsplanungsalgorithmen angewendet und sie beinhalten die Konfiguration von Programmen, welche zu unterschiedlichen algorithmischen Familien gehören. Die Untersuchung der Domäne führte zur Identifizierung der Variabilitätspunkte und der möglichen Variationen. Entsprechende Proof of Concept Implementierungen in Form von Repositories repräsentieren jene Variabilitätspunkte und beziehen diese in ihre semantische Struktur ein. Die gewählten algorithmischen Familien sehen bestimmte Berechnungsschritte oder Datenstrukturen vor, und entsprechende Software Komponenten stellen mögliche Variationen dar. Versuchsergebnisse belegen, dass CLS synthese-getriebene domänenspezifische Optimierungsverfahren ermöglicht, welche komplexe Probleme durch die Exploration von Räumen hoher Variabilität lösen